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Sunseasonal (Extended-Range) Forecast of the Asian Summer Monsoon Rainfall
Song Yang （杨崧）
Department of Atmospheric Sciences
Sun Yat-sen University
Guangzhou, China
Model, Data and Others
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3
2
1
5
Quasi-Biweekly Oscillation
Regional Monsoons
Tropical Land and Ocean Rainfalls
Southern China Early-Season Rainfall
CONTENT
3Coauthors and Reference 3
1. Jia, X. and S. Yang*, 2013: Impacts of the quasi-biweekly oscillation over the western North Pacific on East Asian subtropical monsoon during early summer. J. Geophys. Res., 118, 1-14.2. Jia, X., S. Yang* & Coauthors, 2013: Prediction of global patterns of dominant quasi- biweekly oscillation by the NCEP Climate Forecast System version 2. Climate Dyn., 41, 1635-1650.3. Liu, X., S. Yang* & Coauthors, 2013: Diagnostics of sub-seasonal prediction biases of the Asian summer monsoon by the NCEP Climate Forecast System. Climate Dyn., 41, 1453-1474.4. Liu, X, S. Yang* & Coauthors, 2014: Subseasonal forecast skills of global summer monsoons in the NCEP Climate Forecast System version 2. Climate Dyn., 42, 1487-1508.5. Liu, X., S. Yang* & Coauthors, 2015: Subseasonal predictions of regional summer monsoon rainfalls over tropical Asian oceans and land. J. Climate, submitted.6. Zhao, S. and S. Yang*, 2014: Dynamical prediction of the early-season rainfall over southern China by the NCEP Climate Forecast System. Wea. Forecasting, 29, 1391-1401.7. Zhao, S., S. Yang* & Coauthors, 2015: Skills of yearly prediction of the early-season rainfall over southern China by the NCEP Climate Forecast System. Theor. & Appl. Climatol., in press.
4US NCEP Climate Forecast System (CFSv2)
4
Atmosphere GFS2009 (T126/L64)
Land NOAH 4-L
Ocean MOM4
Sea ice Predicted
CO2Evolving with time
Initial conditions
CFS Reanalysis (CFSR)
Hindcast ~24/month (4 runs / 5 days)
Forecast4 runs/day (seasonal)16 runs/day (45 days)
Hindcast Data Used:
Daily data, 1999-2011
Four members every day, integrated for 45 days
Model, Data and Others
4
3
2
1
5
Quasi-Biweekly Oscillation
Regional Monsoons
Tropical Land and Ocean Rainfalls
Southern China Early-Season Rainfall
CONTENT
6QBWO in AVHRR, CFSR and CFSv2
10-20-day variance of OLR in boreal summer for NOAA AVHRR, CFSR, and CFSv2 predictions at various leads
7South Asia High & E-SE Asia Convection
Quasi-biweekly variability of the South Asia High is important for convergence/divergence over East & Southeast Asia
Contours: H200
Shading: Difference in 200-hPa Divergence
8QBWO Variance
Kikuchi and Wang (2009)
Three JJA Domains
Five DJF Domains
9Prediction of Eight QBWS Modes
High skills for predicting the North Pacific and South Pacific Modes, but low skill for predicting the Asian summer monsoon
Best:
North Pacific South Pacific
Worst:
Asian Monsoon Central America South Africa
10Prediction of QBWO for El Nino & La Nina Years
High skill for El Nino years, but low skill for La Nina years
Model, Data and Others
4
3
2
1
5
Quasi-Biweekly Oscillation
Regional Monsoons
Tropical Land and Ocean Rainfalls
Southern China Early-Season Rainfall
CONTENT
Indian Monsoon & SCS Monsoon
12
Precipitation (70ºE-90ºE) Precipitation (110ºE-130ºE)
More Effect from the Tropical Indian Ocean SST (Boundary Forcing)
More Effect from the Subtropical Western Pacific High (Internal
Dynamics)SST forcing is important for skills of subseasonal (extended-range) forecast of regional monsoon rainfalls
Webster-Yang Index & Goswami et al. Index
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Prediction skill is high when regional monsoon is strongly related to large-scale features
Multivariate EOF Analysis (Rainfall & V850)
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PCs: From short leads (red) to longer leads (blue)
(1) Prediction skill is a function of the stage of monsoons(2) An abrupt turning point of bias in late June and early
July
Model, Data and Others
4
3
2
1
5
Quasi-Biweekly Oscillation
Regional Monsoons
Tropical Land and Ocean Rainfalls
Southern China Early-Season Rainfall
CONTENT
Six Ocean and Land Domains 16
Pattern Correlation 17
Multi-year
Average
Individual Years
Higher skills over ocean domains, esp. the Arabian Sea
Lower skills over land domains, esp. the Indo-China Peninsula
Temporal Correlation 18
Again, higher skills are over ocean domains, esp. the Arabian Sea, and lower skills are over land domains, esp. the Indo-China Peninsula
Corr. between Precip & Ts 19
Positive: Enhanced radiation => increased Ts => unstable => convectionNegative: Enhanced rainfall => declined radiation => decreased Ts
Overestimation worsens with lead time (e.g. Arabian Sea)
Positively significant over Arabian Sea
Negatively significant over land except southern China
Corr with Ts, and Reg of V850 on P Indices20
1. Overestimated relationships of rainfall with Ts and atmospheric circulation
2. Problem worsens with increased lead time
Cross Correlations: Supporting Evidence21
1. Larger correlation appears for neighboring regions compared to more remote regions
2. Longer range predictions capture larger scale features
Lag Corr (Precip/Ts) 22
1. Largest correlations at leads or lags by 1-2 pentads
2. Ts forcing in AS, but Ts response in BOB and SCS
3. Ts response in Indian Peninsula, but weak air-land interaction in southern China
4. Changes with lead time?
Other Features 23
Regional rainfall over oceans is related to larger-scale circulation patterns, compared to that over land
As lead time increases, strengthening connections between regional rainfall and large-scale circulation are found over extensive regions, and the regional independence of rainfall variability is gradually obscured by uniform large-scale features.
Comparisons between skillful and unskillful forecasts indicate that the regional characteristics of rainfall and model’s deficiencies in capturing the relationship between small- and large-scale features are responsible for the regional discrepancies of actual subseasonal predictability.
Model, Data and Others
4
3
2
1
5
Quasi-Biweekly Oscillation
Regional Monsoons
Tropical Land and Ocean Rainfalls
Southern China Early-Season Rainfall
CONTENT
Simulations of SC Early-Season Rainfall25
Rainfall for Pentads 19-36
Pentad Rainfalland
Model-Obs Difference
Prediction of SC Rainfall (Skill of 8-14 Days)
26
Higher Skill for April-May than for June
Higher Skill for Southern China than for Other Regions
Prediction of SC Early-Season Rainfall 27
Difference in Predicted Rainfalls (Lead Time 15-29 Days Minus Lead Time 0-14 Dyas)
Yearly Prediction of SC Early-Season Rainfall
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Higher Skills in 2005 & 2006Lower Skills in 2001 & 2010
Small Difference in LD 0-4 Prediction and LD 0-14 Prediction
Detailed Features for Years of High & Low Skills
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Years of High Skills2005 & 2006
Years of Low Skills2001 & 2010
30Detailed Features for Years of High & Low Skills
High Skills
Low Skills
Prediction of Surface Temp for Pentads 1-12
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High Skills for Rainfall Prediction Low Skills for Rainfall Prediction
In the years with high (low) skills of predicting SC early-season rainfall, the skills of predicting the previous surface temperature over the
tropical western Pacific are high (low).
Summary 32
